A database of predictor test sex bias for development of military physical employment standards
Bibliographic record
Abstract
BACKGROUND: Hesitation to employ females for physically demanding jobs is often due to sex related physical abilities. A physical employment standard (PES) identifies individuals who are physically capable for work. OBJECTIVE: A database containing 300 + sources of physical performance tests (PFTs) will inform potential sex bias for PES development. METHODS: Weighted means and probability density curves illustrate the percentage overlap between male and female performance on PFT data from the armed forces of 11 countries and the open literature. Where female training data were available, the change in percentage overlap illustrates the potential for reduction in sex-related differences. RESULTS: PFTs demonstrating the extremes of sex disparity were bench press (11 sources) and sit-ups (14 sources) with 9% and 93% overlap in performance, respectively. Training for bench press; pull ups; VO2max; and upright pull improved female performance by 12%, 22%, 35%, and 23% respectively. This translated into narrowing the gap between male and female mean performance by 1%, 4%, 5%, and 10% respectively. CONCLUSIONS: The ability of PFT to predict performance is essential; however, PFTs with more overlap will facilitate development of PES with reduced sex bias. PFTs with the greatest potential for improvement in females are identified here.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".